Interactive editing of virtual chordae tendineae for the simulation of the mitral valve in a decision support system

Purpose: Decision support systems for mitral valve disease are an important step toward personalized surgery planning. A simulation of the mitral valve apparatus is required for decision support. Building a model of the chordae tendineae is an essential component of a mitral valve simulation. Due to image quality and artifacts, the chordae tendineae cannot be reliably detected in medical imaging. Methods: Using the position-based dynamics framework, we are able to realistically simulate the opening and closing of the mitral valve. Here, we present a heuristic method for building an initial chordae model needed for a successful simulation. In addition to the heuristic, we present an interactive editor to refine the chordae model and to further improve pathology reproduction as well as geometric approximation of the closed valve. Results: For evaluation, five mitral valves were reconstructed based on image sequences of patients scheduled for mitral valve surgery. We evaluated the approximation of the closed valves using either just the heuristic chordae model or a manually refined model. Using the manually refined models, prolapse was correctly reproduced in four of the five cases compared to two of the five cases when using the heuristic. In addition, using the editor improved the approximation in four cases. Conclusions: Our approach is suitable to create realistically parameterized mitral valve apparatus reconstructions for the simulation of normally and abnormally closing valves in a decision support system

Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data

Background: Gene expression measurements during the development of the fly Drosophila melanogaster are routinely used to find functional modules of temporally co-expressed genes. Complimentary large data sets of in situ RNA hybridization images for different stages of the fly embryo elucidate the spatial expression patterns. Results: Using a semi-supervised approach, constrained clustering with mixture models, we can find clusters of genes exhibiting spatio-temporal similarities in expression, or syn-expression. The temporal gene expression measurements are taken as primary data for which pairwise constraints are computed in an automated fashion from raw in situ images without the need for manual annotation. We investigate the influence of these pairwise constraints in the clustering and discuss the biological relevance of our results. Conclusion: Spatial information contributes to a detailed, biological meaningful analysis of temporal gene expression data. Semi-supervised learning provides a flexible, robust and efficient framework for integrating data sources of differing quality and abundance

Bildbasierte Nachverfolgung, Quantifizierung und Exploration kardialer Dynamik

The heart is the central driver of blood circulation, which supplies the body with oxygen. Heart function is determined by a complex interaction between heart wall contraction, heart valves and respiration. Cardiac diseases and disease progression manifest often regionally and diversely across patients. Modern clinical imaging allows the acquisition of image data for the detailed assessment of the state and dynamics of the heart muscle and the valves. Quantification and advanced analysis of these structures require image processing and image-based modeling techniques to enable the patient-specific evaluation of heart function. The goal of this thesis is to investigate and implement methods for the regional analysis of motion patterns in the heart wall, segmentation and analysis of the mitral valve for quantification and simulation, and for the integrated visualization of time-resolved cardiac multi-parameter results. The developed methods include efficient registration-based wall motion analysis in 3D tagged MRI and echocardiography images, interventricular septum motion analysis in cine MRI images time-resolved by cardiac and respiratory phase, semi-automatic segmentation of the mitral valve in 3D CT images combining user-defined landmarks and image information, automatic segmentation of the mitral valve in 4D echocardiography images by tracking a deformable model, regional subdivision of a mitral valve model for standardized reporting, and an interactive hierarchical exploration approach incorporating multiple parameters and the relevant temporal dimensions to make the quantitative results of analysis methods such as the previous ones accessible to medical experts. All techniques have been evaluated on a variety of phantom and clinical data. The heart wall motion analysis performed with satisfactory accuracy and strain recovery in different modalities. The septum analysis produced consistent motion parameters, and showed the need for an appropriate visualization tool. The evaluation of the semi-automatic valve segmentation demonstrated an efficient method to define a valve model in image data with varying contrast and in the presence of pathologies. The automatic 4D valve segmentation produced satisfactory valve models in normal and pathological cases. The accuracy of the regional subdivision was comparable to the variation between user experts. The interactive exploration approach demonstrated a promising approach that was valued as helpful for the task. Remaining challenges that have to be addressed in future work include the temporal consistency of wall motion analysis, and high inter-operator variation in mitral valve segmentation.Das Herz ist das treibende Organ im Blutkreislauf, der den Körper mit Sauerstoff versorgt. Die Herzfunktion wird durch ein komplexes Zusammenspiel zwischen Wandkontraktion, Herzklappen und Atmung bestimmt. Erkrankungen des Herzens und ihre Verläufe manifestieren sich oft regional und je nach Patient unterschiedlich. Moderne klinische Bildgebung ermöglicht die Aufnahme von Bilddaten für die detaillierte Bewertung von Zustand und Dynamik von Herzmuskel und -klappen. Quantifizierung und weitergehende Analyse dieser Strukturen erfordert Methoden der Bildverarbeitung und bildbasierten Modellierung, um patientenspezifisch die Herzfunktion bewerten zu können. Das Ziel dieser Arbeit ist die Untersuchung und Implementierung von Methoden zur regionalen Analyse von Bewegungsmustern in der Herzwand, zur Segmentierung und Analyse der Mitralklappe zur Quantifizierung und Simulation, und zur integrierten Visualisierung zeitaufgelöster kardialer multiparametrischer Ergebnisse. Die entwickelten Methoden umfassen eine effiziente registrierungsbasierte Wandbewegungsanalyse in 3D-Tagged-MRT- und Echokardiographie-Daten, die Bewegungsanalyse des interventrikulären Septums in Cine-MRI-Bildern mit zeitlicher Auflösung von Herz- und Atemphase, die semi-automatische Segmentierung der Mitralklappe in 3D-CT-Daten durch Kombination von benutzerdefinierten Landmarken und Bildinformationen, die automatische Segmentierung der Mitralklappe in 4D-Echokardiographie-Bildern durch Tracking eines verformbaren Modells, die regionale Unterteilung eines Mitralklappenmodells zur standardisierten Dokumentation, und einen interaktiven hierarchichen multiparametrischen Explorationsansatz, der alle relevanten zeitlichen Dimensionen beinhaltet und damit die quantitativen Ergebnisse von Analysemethoden wie den vorgenannten für medizinische Experten zugänglich machen kann. Alle Verfahren wurden auf einer Vielzahl von synthetischen und klinischen Daten evaluiert. Die Herzwandbewegungsanalyse erbrachte zufriedenstellende Genauigkeit und Strainberechnung in verschiedenen Bildmodalitäten. Die Analyse des Septums ergab konsistente Bewegungsparameter, und zeigte den Bedarf an einem geeigneten Visualisierungswerkzeug auf. Die Evaluierung der semi-automatischen Klappensegmentierung demonstrierte eine effiziente Methode, um in Bilddaten mit wechselndem Kontrast und Pathologien ein Klappenmodell zu definieren. Die automatische 4D-Klappensegmentierung erbrachte zufriedenstellende Klappenmodelle in normalen und pathologischen Fällen. Die Genauigkeit der regionalen Unterteilung war vergleichbar mit der Variation zwischen menschlichen Experten. Der interaktive Explorationsansatz wurde als vielversprechender Ansatz und als hilfreich für die Aufgabe bewertet. Verbleibende Herausforderungen für zukünftige Arbeiten umfassen die zeitliche Konsistenz der Wandbewegungsanalyse, und hohe Inter-Operator-Variation der Mitralklappensegmentierung

A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification

The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the training and application of ML models can support the improvement of the model training and provide relevant information for understanding the classification-relevant data features. The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data. Correction, annotation, and exploration of clinical data and interpretation of results are supported through dedicated interactive visual analytics tools. We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges. In both applications, pre-trained 2D U-Nets are used for segmentation, and classifiers are trained for diagnostic tasks using radiomics features of the segmented anatomical structures. The solution was successfully used to identify outliers in automatic segmentation and image acquisition. The targeted curation and addition of expert annotations improved the performance of the machine learning models. Clinical experts were supported in understanding specific anatomical and functional characteristics of the assigned disease classes

CT-Based Simulation of Left Ventricular Hemodynamics: A Pilot Study in Mitral Regurgitation and Left Ventricle Aneurysm Patients

Background: Cardiac CT (CCT) is well suited for a detailed analysis of heart structures due to its high spatial resolution, but in contrast to MRI and echocardiography, CCT does not allow an assessment of intracardiac flow. Computational fluid dynamics (CFD) can complement this shortcoming. It enables the computation of hemodynamics at a high spatio-temporal resolution based on medical images. The aim of this proposed study is to establish a CCT-based CFD methodology for the analysis of left ventricle (LV) hemodynamics and to assess the usability of the computational framework for clinical practice. Materials and Methods: The methodology is demonstrated by means of four cases selected from a cohort of 125 multiphase CCT examinations of heart failure patients. These cases represent subcohorts of patients with and without LV aneurysm and with severe and no mitral regurgitation (MR). All selected LVs are dilated and characterized by a reduced ejection fraction (EF). End-diastolic and end-systolic image data was used to reconstruct LV geometries with 2D valves as well as the ventricular movement. The intraventricular hemodynamics were computed with a prescribed-motion CFD approach and evaluated in terms of large-scale flow patterns, energetic behavior, and intraventricular washout. Results: In the MR patients, a disrupted E-wave jet, a fragmentary diastolic vortex formation and an increased specific energy dissipation in systole are observed. In all cases, regions with an impaired washout are visible. The results furthermore indicate that considering several cycles might provide a more detailed view of the washout process. The pre-processing times and computational expenses are in reach of clinical feasibility. Conclusion: The proposed CCT-based CFD method allows to compute patient-specific intraventricular hemodynamics and thus complements the informative value of CCT. The method can be applied to any CCT data of common quality and represents a fair balance between model accuracy and overall expenses. With further model enhancements, the computational framework has the potential to be embedded in clinical routine workflows, to support clinical decision making and treatment planning

PHASE-BASED NON-RIGID REGISTRATION OF MYOCARDIAL PERFUSION MRI IMAGE SEQUENCES

The condition of the heart muscle tissue can be inferred by analyzing the time-intensity curves obtained with myocardial perfusion MRI. Specifically, identifying tissue that is undersupplied with blood is important when choosing a suitable therapy for patients with coronary heart disease. Before an analysis can be carried out, the images must be registered to compensate for cardiac and respiratory motion. This is a difficult problem, as the motion is non-rigid and because the image contrast varies strongly over time due to the injection of a contrast agent into the blood stream. To address these problems, an automatic non-rigid registration approach is presented that utilizes local phase instead of intensity or gradient information. Index Terms — perfusion, registration, local phase 1

Interactive editing of virtual chordae tendineae for the simulation of the mitral valve in a decision support system

Purpose Decision support systems for mitral valve disease are an important step toward personalized surgery planning. A simulation of the mitral valve apparatus is required for decision support. Building a model of the chordae tendineae is an essential component of a mitral valve simulation. Due to image quality and artifacts, the chordae tendineae cannot be reliably detected in medical imaging. Methods Using the position-based dynamics framework, we are able to realistically simulate the opening and closing of the mitral valve. Here, we present a heuristic method for building an initial chordae model needed for a successful simulation. In addition to the heuristic, we present an interactive editor to refine the chordae model and to further improve pathology reproduction as well as geometric approximation of the closed valve. Results For evaluation, five mitral valves were reconstructed based on image sequences of patients scheduled for mitral valve surgery. We evaluated the approximation of the closed valves using either just the heuristic chordae model or a manually refined model. Using the manually refined models, prolapse was correctly reproduced in four of the five cases compared to two of the five cases when using the heuristic. In addition, using the editor improved the approximation in four cases. Conclusions Our approach is suitable to create realistically parameterized mitral valve apparatus reconstructions for the simulation of normally and abnormally closing valves in a decision support system.ISSN:1861-6410ISSN:1861-642